Predicting Structure & Clarity of software projects with Machine Learning

2020 ◽  
Author(s):  
Darius Mihnea BOGDAN ◽  
Anca MARGINEAN
Keyword(s):  
Machine Learning ◽  
Software Projects

scholarly journals Estimation Approaches of Machine Learning in Scrum Projects: A Review

2021 ◽  
Vol 9 (11) ◽  
pp. 1110-1118
Author(s):  
Chitrak Vimalbhai Dave
Keyword(s):  
Machine Learning ◽  
Regression Testing ◽  
Process Models ◽  
Effort Estimation ◽  
Software Projects ◽  
It Industry ◽  
Resistance Factors ◽  
User Story ◽  
Inaccurate Estimation ◽  
Agile Software

Abstract: It is inevitable for any successful IT industry not to estimate the effort, cost, and duration of their projects. As evident by Standish group chaos manifesto that approx 43% of the projects are often delivered late and entered crises because of over budget and less required functions. Improper and inaccurate estimation of software projects leads to a failure, and therefore it must be considered in true letter and spirit. When Agile principle-based process models (e.g. Scrum) came into the market, a significant change can be seen. This change in culture proves to be a boon forstrengthening the collaboration betweendeveloper and customer.Estimation has always been challenging in Agile as requirements are volatile. This encourages researchersto work on effort estimation. There are many reasons for the gap between estimated and actual effort, viz., project, people, and resistance factors, wrong use of cost drivers, ignorance of regression testing effort, understandability of user story size and its associated complexity, etc. This paperreviewed the work of numerous authors and potential researchers working on bridging the gap of actual and estimated effort. Through intensive and literature review, it can be inferred that machine learning models clearly outperformed non-machine learning and traditional techniques of estimation. Keywords: Machine Learning, Scrum, Scrum Projects, Effort Estimation, Agile Software Development

Story Point-Based Effort Estimation Model with Machine Learning Techniques

2020 ◽  
Vol 30 (01) ◽  
pp. 43-66
Author(s):  
Muaz Gultekin ◽  
Oya Kalipsiz
Keyword(s):  
Machine Learning ◽  
Empirical Evaluation ◽  
Machine Learning Algorithms ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Effort Estimation ◽  
Estimation Model ◽  
Software Projects ◽  
Learning Techniques ◽  
Better Than

Until now, numerous effort estimation models for software projects have been developed, most of them producing accurate results but not providing the flexibility to decision makers during the software development process. The main objective of this study is to objectively and accurately estimate the effort when using the Scrum methodology. A dynamic effort estimation model is developed by using regression-based machine learning algorithms. Story point as a unit of measure is used for estimating the effort involved in an issue. Projects are divided into phases and the phases are respectively divided into iterations and issues. Effort estimation is performed for each issue, then the total effort is calculated with aggregate functions respectively for iteration, phase and project. This architecture of our model provides flexibility to decision makers in any case of deviation from the project plan. An empirical evaluation demonstrates that the error rate of our story point-based estimation model is better than others.

scholarly journals A Method for Recommending Bug Fixer Using Community Q&A Information

2018 ◽  
Vol 173 ◽  
pp. 03031
Author(s):  
Qingjie Wei ◽  
Jiao Liu ◽  
Jun Chen
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Open Source Software ◽  
Text Classification ◽  
Classification Problem ◽  
Software Projects ◽  
Bug Fixing ◽  
Bug Report ◽  
Information Retrieval Methods ◽  
Time Aware

It is a very time-consuming task to assign a bug report to the most suitable fixer in large open source software projects. Therefore, it is very necessary to propose an effective recommendation method for bug fixer. Most research in this area translate it into a text classification problem and use machine learning or information retrieval methods to recommend the bug fixer. These methods are complex and overdependent on the fixers’ prior bug-fixing activities. In this paper, we propose a more effective bug fixer recommendation method which uses the community Q & A platforms (such as Stack Overflow) to measure the fixers’ expertise and uses the fixed bugs to measure the time-aware of fixers’ fixed work. The experimental results show that the proposed method is more accurate than most of current restoration methods.

Research on Software Defect Prediction Method Based on Machine Learning

2014 ◽  
Vol 687-691 ◽  
pp. 2182-2185 ◽  
Author(s):  
Wei Zhang ◽  
Zhen Yu Ma ◽  
Qing Ling Lu ◽  
Xiao Bing Nie ◽  
Juan Liu
Keyword(s):  
Machine Learning ◽  
Correlation Analysis ◽  
Software Metrics ◽  
Defect Density ◽  
Rank Correlation ◽  
Prediction Method ◽  
Learning Models ◽  
Software Projects ◽  
Software Defect ◽  
Machine Learning Models

This paper analyzed 44 metrics of application level, file level, class level and function level, and do correlation analysis with the number of software defects and defect density, the results show that software metrics have little correlation with the number of software defect, but are correlative with defect density. Through correlation analysis, we selected five metrics that have larger correlation with defect density. On the basis of feature selection, we predicted defect density with 16 machine learning models for 33 actual software projects. The results show that the Spearman rank correlation coefficient (SRCC) between the predicting defect density and the actual defect density based on SVR model is 0.6727, higher than other 15 machine learning models, the model that has the second absolute value of SRCC is IBk model, the SRCC only is-0.3557, the results show that the method based on SVR has the highest prediction accuracy.

scholarly journals Prediction of Software Design Defect using Enhanced Machine Learning Techniques

2020 ◽  
Vol 8 (5) ◽  
pp. 2462-2465
Keyword(s):  
Machine Learning ◽  
Software Quality ◽  
Software Design ◽  
Software Package ◽  
Machine Learning Techniques ◽  
Software Project ◽  
Software Projects ◽  
Design Defect ◽  
Learning Techniques ◽  
The Cost

Prediction of software detection is most widely used in many software projects and this will improve the software quality, reducing the cost of the software project. It is very important for the developers to check every package and code files within the project. There are two classifiers that are present in the Software Package Defect (SPD) prediction that can be divided as Defect–prone and not-defect-prone modules. In this paper, the merging of Cost-Sensitive Variance Score (CSVS), Cost-Sensitive craniologist Score (CSLS) and Cost-Sensitive Constraint Score (CSCS). The comparitive analysis can be shown in between the three algorithms and also individually.

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